Introduction

This report investigates the “Red Wine Quality” dataset which is related to red variants of the Portuguese “Vinho Verde” wine…

Dataset Exploration

Let’s have a look at a summery of the dataset.

## Rows: 1,599
## Columns: 12
## $ `fixed acidity`        <dbl> 7.4, 7.8, 7.8, 11.2, 7.4, 7.4, 7.9, 7.3, 7.8, 7…
## $ `volatile acidity`     <dbl> 0.700, 0.880, 0.760, 0.280, 0.700, 0.660, 0.600…
## $ `citric acid`          <dbl> 0.00, 0.00, 0.04, 0.56, 0.00, 0.00, 0.06, 0.00,…
## $ `residual sugar`       <dbl> 1.9, 2.6, 2.3, 1.9, 1.9, 1.8, 1.6, 1.2, 2.0, 6.…
## $ chlorides              <dbl> 0.076, 0.098, 0.092, 0.075, 0.076, 0.075, 0.069…
## $ `free sulfur dioxide`  <dbl> 11, 25, 15, 17, 11, 13, 15, 15, 9, 17, 15, 17, …
## $ `total sulfur dioxide` <dbl> 34, 67, 54, 60, 34, 40, 59, 21, 18, 102, 65, 10…
## $ density                <dbl> 0.9978, 0.9968, 0.9970, 0.9980, 0.9978, 0.9978,…
## $ pH                     <dbl> 3.51, 3.20, 3.26, 3.16, 3.51, 3.51, 3.30, 3.39,…
## $ sulphates              <dbl> 0.56, 0.68, 0.65, 0.58, 0.56, 0.56, 0.46, 0.47,…
## $ alcohol                <dbl> 9.4, 9.8, 9.8, 9.8, 9.4, 9.4, 9.4, 10.0, 9.5, 1…
## $ quality                <dbl> 5, 5, 5, 6, 5, 5, 5, 7, 7, 5, 5, 5, 5, 5, 5, 5,…
Data summary
Name wine
Number of rows 1599
Number of columns 12
_______________________
Column type frequency:
numeric 12
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
fixed acidity 0 1 8.32 1.74 4.60 7.10 7.90 9.20 15.90 ▂▇▂▁▁
volatile acidity 0 1 0.53 0.18 0.12 0.39 0.52 0.64 1.58 ▅▇▂▁▁
citric acid 0 1 0.27 0.19 0.00 0.09 0.26 0.42 1.00 ▇▆▅▁▁
residual sugar 0 1 2.54 1.41 0.90 1.90 2.20 2.60 15.50 ▇▁▁▁▁
chlorides 0 1 0.09 0.05 0.01 0.07 0.08 0.09 0.61 ▇▁▁▁▁
free sulfur dioxide 0 1 15.87 10.46 1.00 7.00 14.00 21.00 72.00 ▇▅▁▁▁
total sulfur dioxide 0 1 46.47 32.90 6.00 22.00 38.00 62.00 289.00 ▇▂▁▁▁
density 0 1 1.00 0.00 0.99 1.00 1.00 1.00 1.00 ▁▃▇▂▁
pH 0 1 3.31 0.15 2.74 3.21 3.31 3.40 4.01 ▁▅▇▂▁
sulphates 0 1 0.66 0.17 0.33 0.55 0.62 0.73 2.00 ▇▅▁▁▁
alcohol 0 1 10.42 1.07 8.40 9.50 10.20 11.10 14.90 ▇▇▃▁▁
quality 0 1 5.64 0.81 3.00 5.00 6.00 6.00 8.00 ▁▇▇▂▁


In the summary above we see a sample of each of the attributes of the dataset. We, also, see the distribution of each attribute represented by the standard deviation (sd), minimum (p0), lower [25th percentile] (p1), median [50th percentile] (p2), upper [75th percentile] (p3), and maximum [100th percentile] (p5) of each attribute.

The following table shows random 10 rows sample of the dataset.

fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
7.4 0.70 0.00 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 5
7.8 0.88 0.00 2.6 0.098 25 67 0.9968 3.20 0.68 9.8 5
7.8 0.76 0.04 2.3 0.092 15 54 0.9970 3.26 0.65 9.8 5
11.2 0.28 0.56 1.9 0.075 17 60 0.9980 3.16 0.58 9.8 6
7.4 0.70 0.00 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 5
7.4 0.66 0.00 1.8 0.075 13 40 0.9978 3.51 0.56 9.4 5
7.9 0.60 0.06 1.6 0.069 15 59 0.9964 3.30 0.46 9.4 5
7.3 0.65 0.00 1.2 0.065 15 21 0.9946 3.39 0.47 10.0 7
7.8 0.58 0.02 2.0 0.073 9 18 0.9968 3.36 0.57 9.5 7
7.5 0.50 0.36 6.1 0.071 17 102 0.9978 3.35 0.80 10.5 5

To further investigate the different variables, let’s have a look at graph representations of them.

Starting by a distribution graph of the red wine quality rantings.


## [1] 0
## Warning in min(x, na.rm = na.rm): no non-missing arguments to min; returning Inf
## Warning in max(x, na.rm = na.rm): no non-missing arguments to max; returning
## -Inf

The above graph suggests that the red wine quality ratings is symmetrically distributed.

If we consider the wine quality rating less than the median (6 ranting) are poor wine, then lets see how many poor wine versus good wine we have in the dataset.

From the binary red wine quality plot we observe that the number of bad quality wine is close to good once. This observation

Next, let’s look at the correlations between the different attributes.

From this heat-map one can observe that the attributes that have the highest correlations with wine quality are alcohol, volatile acidity and sulphates. So let’s have a closer look at them.

Let’s take a look at the relationship between alcohol, sulphates and quality.

Conclusion

In conclusion, a good quality red wine has a strong correction with alcohol, volatile acidity and sulphates. In order to have a good quality red wine volatile acidity and sulphates need to be at minimum unlike alcohol which need to be in the higher range